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Learning Fast and Slow for Online Time Series Forecasting
[article]
2022
arXiv
pre-print
The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting. Successful solutions require handling changes to new and recurring patterns. However, training deep neural forecaster on the fly is notoriously challenging because of their limited ability to adapt to non-stationary environments and the catastrophic forgetting of old knowledge. In this work, inspired by the Complementary Learning Systems (CLS) theory, we propose
arXiv:2202.11672v2
fatcat:qu63p3fi5feynck7cbu6fspgt4